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Difference between Processing and Preprocessing

Difference between Processing and Preprocessing


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In the wikipedia article of emotion lateralization it is mentioned:

The right hemisphere is important for processing primary emotions such as fear while the left hemisphere is important for preprocessing social emotions

So I am wondering what exactly processing and preprocessing mean? What happens in preprocessing that they use this distinct word instead of mere processing?


Why Image Preprocessing and Augmentation Matter

This old machine learning adage conveys a salient machine learning point: unless input data is of high quality, model accuracy — even with the best computer vision architectures — will suffer.

But what’s forgotten is how much control data scientists, developers, and computer vision engineers have over input data, even if not the principal agent collecting the data. What’s more: steps taken in the image input pipeline can turn what was once high quality data into lower signal-producing inputs.

This is not to say data quality is not an a priori concern. Striving to collect high quality data for the task at hand is always important. But there are instances where deep learning engineers may blindly apply preprocessing and augmentation steps that reduce model performance on the same data. And, even when having high quality data, preprocessing allows the best possible results to be obtained.

Understanding what preprocessing and augmentation are at their core enables data scientists to get the most out of their input data.


A Data Scientist's Guide to 8 Types of Sampling Techniques

Overview Sampling is a popular statistical concept - learn how it works in this article We will also talk about eight…

Most real world datasets have a large number of features. For example, consider an image processing problem, we might have to deal with thousands of features, also called as dimensions. As the name suggests, dimensionality reduction aims to reduce the number of features - but not simply by selecting a sample of features from the feature-set, which is something else — Feature Subset Selection or simply Feature Selection.

Conceptually, dimension refers to the number of geometric planes the dataset lies in, which could be high so much so that it cannot be visualized with pen and paper. More the number of such planes, more is the complexity of the dataset.

The Curse of Dimensionality
This refers to the phenomena that generally data analysis tasks become significantly harder as the dimensionality of the data increases. As the dimensionality increases, the number planes occupied by the data increases thus adding more and more sparsity to the data which is difficult to model and visualize.

What dimension reduction essentially does is that it maps the dataset to a lower-dimensional space, which may very well be to a number of planes which can now be visualized, say 2D. The basic objective of techniques which are used for this purpose is to reduce the dimensionality of a dataset by creating new features which are a combination of the old features. In other words, the higher-dimensional feature-space is mapped to a lower-dimensional feature-space. Principal Component Analysis and Singular Value Decomposition are two widely accepted techniques.

A few major benefits of dimensionality reduction are :

  • Data Analysis algorithms work better if the dimensionality of the dataset is lower. This is mainly because irrelevant features and noise have now been eliminated.
  • The models which are built on top of lower dimensional data are more understandable and explainable.
  • The data may now also get easier to visualize!
    Features can always be taken in pairs or triplets for visualization purposes, which makes more sense if the featureset is not that big.

As mentioned before, the whole purpose of data preprocessing is to encode the data in order to bring it to such a state that the machine now understands it.

Feature encoding is basically performing transformations on the data such that it can be easily accepted as input for machine learning algorithms while still retaining its original meaning.

There are some general norms or rules which are followed when performing feature encoding. For Continuous variables :

  • Nominal : Any one-to-one mapping can be done which retains the meaning. For instance, a permutation of values like in One-Hot Encoding.
  • Ordinal : An order-preserving change of values. The notion of small, medium and large can be represented equally well with the help of a new function, that is, <new_value = f(old_value)> - For example, <0, 1, 2>or maybe <1, 2, 3>.

  • Interval : Simple mathematical transformation like using the equation <new_value = a*old_value + b>, a and b being constants. For example, Fahrenheit and Celsius scales, which differ in their Zero values size of a unit, can be encoded in this manner.
  • Ratio : These variables can be scaled to any particular measures, of course while still maintaining the meaning and ratio of their values. Simple mathematical transformations work in this case as well, like the transformation <new_value = a*old_value>. For, length can be measured in meters or feet, money can be taken in different currencies.

Train / Validation / Test Split

After feature encoding is done, our dataset is ready for the exciting machine learning algorithms!
But before we start deciding the algorithm which should be used, it is always advised to split the dataset into 2 or sometimes 3 parts. Machine Learning algorithms, or any algorithm for that matter, has to be first trained on the data distribution available and then validated and tested, before it can be deployed to deal with real-world data.

Training data : This is the part on which your machine learning algorithms are actually trained to build a model. The model tries to learn the dataset and its various characteristics and intricacies, which also raises the issue of Overfitting v/s Underfitting.

Validation data : This is the part of the dataset which is used to validate our various model fits. In simpler words, we use validation data to choose and improve our model hyperparameters. The model does not learn the validation set but uses it to get to a better state of hyperparameters.

Test data : This part of the dataset is used to test our model hypothesis. It is left untouched and unseen until the model and hyperparameters are decided, and only after that the model is applied on the test data to get an accurate measure of how it would perform when deployed on real-world data.

Split Ratio : Data is split as per a split ratio which is highly dependent on the type of model we are building and the dataset itself. If our dataset and model are such that a lot of training is required, then we use a larger chunk of the data just for training purposes (usually the case) — For instance, training on textual data, image data, or video data usually involves thousands of features!
If the model has a lot of hyperparameters that can be tuned, then keeping a higher percentage of data for the validation set is advisable. Models with less number of hyperparameters are easy to tune and update, and so we can keep a smaller validation set.
Like many other things in Machine Learning, the split ratio is highly dependent on the problem we are trying to solve and must be decided after taking into account all the various details about the model and the dataset in hand.

In this article, I wanted to give a solid introduction to the concepts of data preprocessing which is a crucial step in any Machine Learning process. I hope this was useful to you.
Let me know in the comments if there is any feedback!

If you want to see more of such articles head over to The Data Science Portal


Difference between Processing and Preprocessing - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


Conclusions

In summary, we developed a reliable peak-calling analysis pipeline named STARRPeaker that is optimized for large-scale STARR-seq experiments. To illustrate the utility of our method, we applied it to two whole human genome STARR-seq datasets from K562 and HepG2 cell lines, utilizing ORI-based plasmids.

STARRPeaker has several key improvements over previous approaches including (1) precise and efficient calculation of fragment coverage (2) accurate modeling of the basal transcription rate using negative binomial regression and (3) accounting for potential confounding factors, such as GC content, mappability, and the thermodynamic stability of genomic libraries. We demonstrate the superiority of our method over previously used peak callers, supported by strong enrichment of epigenetic marks relevant to enhancers and overlap with previously known enhancers.

To fully understand how noncoding regulatory elements can modulate transcriptional programs in human, STARR-seq active regions must be further characterized and validated within different cellular contexts. For example, recent applications of CRISPR-dCas9 to genome editing have allowed researchers to epigenetically perturb and test these elements in their native genomic context [45, 46]. The next step for CRISPR-based functional screens is to overcome the current limitation of a small scale by leveraging barcodes and single-cell sequencing technology [47]. In the meantime, we envision that the STARRPeaker framework could be utilized to detect and quantify enhancers at the whole-genome level, thereby aiding in prioritizing candidate regions in an unbiased fashion to maximize functional characterization efforts.


Step 3:

Still, inside the function Processing() we add this code to smooth our image to remove unwanted noise. We do this using gaussian blur.

Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function. It is a widely used effect in graphics software, typically to reduce image noise. The visual effect of this blurring technique is a smooth blur resembling that of viewing the image through a translucent screen, distinctly different from the bokeh effect produced by an out-of-focus lens or the shadow of an object under usual illumination. Gaussian smoothing is also used as a pre-processing stage in computer vision algorithms in order to enhance image structures at different scales.


Difference between Processing and Preprocessing - Biology

Preprocessing & Post processing

Preprocessing- data performed before storage into receiver.

Postprocessing - manipulation of the data after storage in the scan converter.

  • uses old data,
  • larger pixel size,
  • same number of pixels,
  • unchanged SR,
  • unchanged TR,
  • Before storage in the scan converter
  • Scans anatomy creates image
  • Image converted from analog to digital
  • Sonographer Identify ROI. System discards existing data in scan converter
  • US rescans only the ROI & writes new data into scan converter
  • Image used to identify ROI is discarded
  • All new info acquired
  • #of pixels, scan lines in ROI is greater than ROI of original image

· More pixels=better spatial resolution

· Pixels are the same size as original

  • improves image quality,
  • higher signal to noise ratio,
  • improved ar,
  • impoved sr,
  • improved cr,
  • deeper penetration,

Pixel Interpolation/ Fill-in Interpolation (Preprocessing)

A way of filling in gaps of data undetected by the observer


Difference between Prokaryotic and Eukaryotic Protein Synthesis

In prokaryotes protein synthesis begins even before the transcription of mRNA molecule is completed. This is called coupled transcription - translation.


2. Eukaryotic mRNA molecules are monocistronic , containing the coding sequence only for one polypeptide.
In prokaryotes individual bacterial mRNA molecules are polycistronic having transcripts of several genes of a particular metabolic pathway. ( Monocistronic vs polycistronic )

3. In eukaryotes, most of the gene have introns that separate the actual message for the synthesis of one protein into small coding segment called exons.
Prokaryotes do not have introns (Except Archaebacteria).

  • About ten initiating factors(IFs ) have been identified in reticulocytes an RBC. These are eIF1, eIF2, eIF3, eIF4 , eIF5, eIF6 ,eIF4B, eIF4C,eIF4D, eIF4F
  • Three initiating factors found in prokaryotes. PIF-1 , PIF-2 , PIF-3.

8. In eukaryotes 5’cap initiates translation by binding mRNA to small ribosomal subunit usually at the first codon AUG.
In bacteria translation begins at an AUG codon preceded by a special nucleotide sequence.

9. A poly A tail formed of about 200 adenine nucleotides is added at the 3’end of mRNA in Eukaryotes.
No poly A tail is added to bacterial mRNA.

10. In eukaryotes small subunit of ribosome(40 S) gets dissociated with the initatior amino acyl tRNA (Met-tRNA Met) without the help of mRNA. The complex joins mRNA later on.
In prokaryotes 30 S subunit first complexes with mRNA (30S-mRNA) then joins with f Met tRNA f-Met.


  • Kong et al. (2015) K. Kong, C. Kendall, N. Stone, and I. Notingher, “Raman spectroscopy for medical diagnostics — From in-vitro biofluid assays to in-vivo cancer detection,” Advanced Drug Delivery Reviews 89 , 121–134 (2015).
  • Enejder et al. (2002) A. M. K. Enejder, T.-W. Koo, J. Oh, M. Hunter, S. Sasic, M. S. Feld, and G. L. Horowitz, “Blood analysis by Raman spectroscopy,” Optics Letters 27 , 2004 (2002).
  • Mak et al. (2013) J. S. Mak, S. A. Rutledge, R. M. Abu-Ghazalah, F. Eftekhari, J. Irizar, N. C. Tam, G. Zheng, and A. S. Helmy, “Recent developments in optofluidic-assisted Raman spectroscopy,” Progress in Quantum Electronics 37 , 1–50 (2013).
  • Jolliffe and Cadima (2016) I. T. Jolliffe and J. Cadima, “Principal component analysis: a review and recent developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374 , 20150202 (2016).
  • Jarvis and Goodacre (2005)

R. M. Jarvis and R. Goodacre, “Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data,”

R. B. Keithley, R. M. Carelli, and R. M. Wightman, “Rank Estimation and the Multivariate Analysis of in Vivo Fast-Scan Cyclic Voltammetric Data,”

S. Adusumilli, D. Bhatt, H. Wang, V. Devabhaktuni, and P. Bhattacharya, “A novel hybrid approach utilizing principal component regression and random forest regression to bridge the period of GPS outages,”

Q. Guo, W. Wu, and D. Massart, “The robust normal variate transform for pattern recognition with near-infrared data,”

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Zabbix Documentation 5.4

Preprocessing allows to define transformation rules for the received item values. One or several transformations are possible before saving to the database.

Transformations are executed in the order in which they are defined. Preprocessing is done by Zabbix server or proxy (if items are monitored by proxy).

Note that the conversion to desired value type (as defined in item configuration) is performed at the end of the preprocessing pipeline conversions, however, may also take place if required by the corresponding preprocessing step. See preprocessing details for more technical information.

Configuration

Preprocessing rules are defined in the Preprocessing tab of the item configuration form.

An item will become unsupported if any of the preprocessing steps fails, unless custom error handling has been specified using a Custom on fail option for supported transformations.

For log items, log metadata (without value) will always reset item unsupported state and make item supported again, even if the initial error occurred after receiving a log value from agent.

User macros and user macros with context are supported in item value preprocessing parameters, including JavaScript code.

Type
TransformationDescription
Text
Regular expressionMatch the value to the <pattern> regular expression and replace value with <output>. The regular expression supports extraction of maximum 10 captured groups with the N sequence. Failure to match the input value will make the item unsupported.
Parameters:
pattern - regular expression
output - output formatting template. An N (where N=1…9) escape sequence is replaced with the Nth matched group. A escape sequence is replaced with the matched text.
Please refer to regular expressions section for some existing examples.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value or set a specified error message.
ReplaceFind the search string and replace it with another (or nothing). All occurrences of the search string will be replaced.
Parameters:
search string - the string to find and replace, case-sensitive (required)
replacement - the string to replace the search string with. The replacement string may also be empty effectively allowing to delete the search string when found.
It is possible to use escape sequences to search for or replace line breaks, carriage return, tabs and spaces " s" backslash can be escaped as "" and escape sequences can be escaped as " ". Escaping of line breaks, carriage return, tabs is automatically done during low-level discovery.
TrimRemove specified characters from the beginning and end of the value.
Right trimRemove specified characters from the end of the value.
Left trimRemove specified characters from the beginning of the value.
Structured data
XML XPathExtract value or fragment from XML data using XPath functionality.
For this option to work, Zabbix server must be compiled with libxml support.
Examples:
number(/document/item/value) will extract 10 from <document><item><value>10</value></item></document>
number(/document/item/@attribute) will extract 10 from <document><item attribute="10"></item></document>
/document/item will extract <item><value>10</value></item> from <document><item><value>10</value></item></document>
Note that namespaces are not supported.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error-handling options: either to discard the value, set a specified value or set a specified error message.
JSON PathExtract value or fragment from JSON data using JSONPath functionality.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error-handling options: either to discard the value, set a specified value or set a specified error message.
CSV to JSONConvert CSV file data into JSON format.
For more information, see: CSV to JSON preprocessing.
XML to JSONConvert data in XML format to JSON.
For more information, see: Serialization rules.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error-handling options: either to discard the value, set a specified value or set a specified error message.
Arithmetic
Custom multiplierMultiply the value by the specified integer or floating-point value.
Use this option to convert values received in KB, MBps, etc into B, Bps. Otherwise Zabbix cannot correctly set prefixes (K, M, G etc).
Note that if the item type of information is Numeric (unsigned), incoming values with a fractional part will be trimmed (i.e. Ɔ.9' will become Ɔ') before the custom multiplier is applied.
Supported: scientific notation, for example, 1e+70 (since version 2.2) user macros and LLD macros (since version 4.0) strings that include macros, for example, <#MACRO>e+10 , <$MACRO1>e+ <$MACRO2>(since version 5.2.3)
The macros must resolve to an integer or a floating-point number.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Change
Simple changeCalculate the difference between the current and previous value.
Evaluated as value-prev_value, where
value - current value prev_value - previously received value
This setting can be useful to measure a constantly growing value. If the current value is smaller than the previous value, Zabbix discards that difference (stores nothing) and waits for another value.
Only one change operation per item is allowed.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Change per secondCalculate the value change (difference between the current and previous value) speed per second.
Evaluated as (value-prev_value)/(time-prev_time), where
value - current value prev_value - previously received value time - current timestamp prev_time - timestamp of previous value.
This setting is extremely useful to get speed per second for a constantly growing value. If the current value is smaller than the previous value, Zabbix discards that difference (stores nothing) and waits for another value. This helps to work correctly with, for instance, a wrapping (overflow) of 32-bit SNMP counters.
Note: As this calculation may produce floating-point numbers, it is recommended to set the 'Type of information' to Numeric (float), even if the incoming raw values are integers. This is especially relevant for small numbers where the decimal part matters. If the floating-point values are large and may exceed the 'float' field length in which case the entire value may be lost, it is actually suggested to use Numeric (unsigned) and thus trim only the decimal part.
Only one change operation per item is allowed.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Numeral systems
Boolean to decimalConvert the value from boolean format to decimal. The textual representation is translated into either 0 or 1. Thus, 'TRUE' is stored as 1 and 'FALSE' is stored as 0. All values are matched in a case-insensitive way. Currently recognized values are, for:
TRUE - true, t, yes, y, on, up, running, enabled, available, ok, master
FALSE - false, f, no, n, off, down, unused, disabled, unavailable, err, slave
Additionally, any non-zero numeric value is considered to be TRUE and zero is considered to be FALSE.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Octal to decimalConvert the value from octal format to decimal.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Hexadecimal to decimalConvert the value from hexadecimal format to decimal.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Custom scripts
JavaScriptEnter JavaScript code in the block that appears when clicking in the parameter field or on a pencil icon.
Note that available JavaScript length depends on the database used.
For more information, see: Javascript preprocessing.
Validation
In rangeDefine a range that a value should be in by specifying minimum/maximum values (inclusive).
Numeric values are accepted (including any number of digits, optional decimal part and optional exponential part, negative values). User macros and low-level discovery macros can be used. The minimum value should be less than the maximum.
At least one value must exist.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Matches regular expressionSpecify a regular expression that a value must match.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Does not match regular expressionSpecify a regular expression that a value must not match.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Check for error in JSONCheck for an application-level error message located at JSONpath. Stop processing if succeeded and the message is not empty otherwise, continue processing with the value that was before this preprocessing step. Note that these external service errors are reported to the user as is, without adding preprocessing step information.
No error will be reported in case of failing to parse invalid JSON.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Check for error in XMLCheck for an application-level error message located at XPath. Stop processing if succeeded and the message is not empty otherwise, continue processing with the value that was before this preprocessing step. Note that these external service errors are reported to the user as is, without adding preprocessing step information.
No error will be reported in case of failing to parse invalid XML.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Check for error using a regular expressionCheck for an application-level error message using a regular expression. Stop processing if succeeded and the message is not empty otherwise, continue processing with the value that was before this preprocessing step. Note that these external service errors are reported to the user as is, without adding preprocessing step information.
Parameters:
pattern - regular expression
output - output formatting template. An N (where N=1…9) escape sequence is replaced with the Nth matched group. A escape sequence is replaced with the matched text.
If you mark the Custom on fail checkbox, the item will not become unsupported in case of failed preprocessing step and it is possible to specify custom error handling options: either to discard the value, set a specified value, or set a specified error message.
Check for not supported valueCheck if there was an error in retrieving item value. Normally that would lead to the item turning unsupported, but you may modify that behavior by specifying the Custom on fail error-handling options: to discard the value, to set a specified value (in this case the item will stay supported and the value can be used in triggers) or set a specified error message. Note that for this preprocessing step, the Custom on fail checkbox is grayed out and always marked.
This step is always executed as the first preprocessing step and is placed above all others after saving changes to the item. It can be used only once.
Supported since 5.2.0.
Throttling
Discard unchangedDiscard a value if it has not changed.
If a value is discarded, it is not saved in the database and Zabbix server has no knowledge that this value was received. No trigger expressions will be evaluated, as a result, no problems for related triggers will be created/resolved. Functions will work only based on data that is actually saved in the database. As trends are built based on data in the database, if there is no value saved for an hour then there will also be no trends data for that hour.
Only one throttling option can be specified for an item.
Note that it is possible for items monitored by Zabbix proxy that very small value differences (less than 0.000001) are correctly not discarded by proxy, but are stored in the history as the same value if the Zabbix server database has not been upgraded.
Discard unchanged with heartbeatDiscard a value if it has not changed within the defined time period (in seconds).
Positive integer values are supported to specify the seconds (minimum - 1 second). Time suffixes can be used in this field (e.g. 30s, 1m, 2h, 1d). User macros and low-level discovery macros can be used in this field.
If a value is discarded, it is not saved in the database and Zabbix server has no knowledge that this value was received. No trigger expressions will be evaluated, as a result, no problems for related triggers will be created/resolved. Functions will work only based on data that is actually saved in the database. As trends are built based on data in the database, if there is no value saved for an hour then there will also be no trends data for that hour.
Only one throttling option can be specified for an item.
Note that it is possible for items monitored by Zabbix proxy that very small value differences (less than 0.000001) are correctly not discarded by proxy, but are stored in the history as the same value if the Zabbix server database has not been upgraded.
Prometheus
Prometheus patternUse the following query to extract required data from Prometheus metrics.
See Prometheus checks for more details.
Prometheus to JSONConvert required Prometheus metrics to JSON.
See Prometheus checks for more details.

Testing

Testing preprocessing steps is useful to make sure that complex preprocessing pipelines yield the results that are expected from them, without waiting for the item value to be received and preprocessed.

Each preprocessing step can be tested individually as well as all steps can be tested together. When you click on the Test or Test all steps button respectively in the Actions block, a testing window is opened.

Testing hypothetical value

ParameterDescription
Get value from hostIf you want to test a hypothetical value, leave this checkbox unmarked.
See also: Testing real value.
ValueEnter the input value to test.
Clicking in the parameter field or on the view/edit button will open a text area window for entering the value or code block.
Not supportedMark this checkbox to test an unsupported value.
This option is useful to test the Check for not supported value preprocessing step.
TimeTime of the input value is displayed: now (read-only).
Previous valueEnter a previous input value to compare to.
Only for Change and Throttling preprocessing steps.
Previous timeEnter the previous input value time to compare to.
Only for Change and Throttling preprocessing steps.
The default value is based on the 'Update interval' field value of the item (if Ƈm', then this field is filled with now-1m ). If nothing is specified or the user has no access to the host, the default is now-30s .
MacrosIf any macros are used, they are listed along with their values. The values are editable for testing purposes, but the changes will only be saved within the testing context.
End of line sequenceSelect the end of line sequence for multiline input values:
LF - LF (line feed) sequence
CRLF - CRLF (carriage-return line-feed) sequence.
Preprocessing stepsPreprocessing steps are listed the testing result is displayed for each step after the Test button is clicked.
If the step failed in testing, an error icon is displayed. The error description is displayed on mouseover.
In case “Custom on fail” is specified for the step and that action is performed, a new line appears right after the preprocessing test step row, showing what action was done and what outcome it produced (error or value).
ResultThe final result of testing preprocessing steps is displayed in all cases when all steps are tested together (when you click on the Test all steps button).
The type of conversion to the value type of the item is also displayed, for example Result converted to Numeric (unsigned) .

Click on Test to see the result after each preprocessing step.

Test values are stored between test sessions for either individual steps or all steps, allowing the user to change preprocessing steps or item configuration and then return to the testing window without having to re-enter information. Values are lost on a page refresh though.

The testing is done by Zabbix server. The frontend sends a corresponding request to the server and waits for the result. The request contains the input value and preprocessing steps (with expanded user macros). For Change and Throttling steps, an optional previous value and time can be specified. The server responds with results for each preprocessing step.

All technical errors or input validation errors are displayed in the error box at the top of the testing window.

Testing real value

To test preprocessing against a real value:

If you have specified a value mapping in the item configuration form ('Show value' field), the item test dialog will show another line after the final result, named 'Result with value map applied'.


Watch the video: 06: Vorverarbeitung: Systeme, Dirac Distribution, Fouriertransformation, Abtastung (January 2023).